Import Packages¶
In [1]:
import numpy as np
import cv2
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import glob
import os
%matplotlib inline
Tracking Class¶
In [2]:
class Line():
def __init__(self):
# was the line detected in the last iteration?
self.detected = False
# polynomial coefficients for the most recent fit
self.current_fit = [np.array([False])]
left_line = Line()
right_line = Line()
Camera Calibration¶
Distortion correction¶
In [3]:
def cal_undistort(img, objpoints, imgpoints):
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1], None, None)
undist = cv2.undistort(img, mtx, dist, None, mtx)
return undist
Calibration¶
In [4]:
# Read in an image
images = glob.glob('camera_cal/calibration*.jpg')
images.sort()
# Arrays to store object points and image points from all the images.
objpoints = [] # 3d point in real world space
imgpoints = [] # 2d points in image plane.
# prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(8,5,0)
objp = np.zeros((6*9,3), np.float32)
objp[:,:2] = np.mgrid[0:9,0:6].T.reshape(-1,2)
for image in images:
img = cv2.imread(image)
# Convert to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# Find the chessboard corners
ret, corners = cv2.findChessboardCorners(gray, (9, 6), None)
# If found, add object points, image points
if ret == True:
imgpoints.append(corners)
objpoints.append(objp)
# Draw and display the corners
img = cv2.drawChessboardCorners(img, (9, 6), corners, ret)
else:
print(image, ": false")
plt.figure()
plt.title(image)
plt.imshow(img)
Result¶
In [5]:
img = cv2.imread("camera_cal/calibration1.jpg")
plt.figure()
plt.title("before calibration:")
plt.imshow(img)
plt.savefig("output_images/before_calibration.jpg")
undistorted = cal_undistort(img, objpoints, imgpoints)
plt.figure()
plt.title("after calibration:")
plt.imshow(undistorted)
plt.savefig("output_images/after_calibration.jpg")
Pipeline (test images)¶
Binary image: combine color and gradient thresholding¶
In [6]:
def binary_image(img,
l_thresh=(200, 255), u_threshold = (0,255), v_threshold = (0,255),
lab_threshold = (0,255), a_threshold = (0,255), b_thresh=(155,200),
sx_thresh=(70, 210)):
# Convert to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0) # Take the derivative in x
abs_sobelx = np.absolute(sobelx) # Absolute x derivative to accentuate lines away from horizontal
scaled_sobel = np.uint8(255*abs_sobelx/np.max(abs_sobelx))
# x gradient threshold
sxbinary = np.zeros_like(scaled_sobel)
sxbinary[(scaled_sobel >= sx_thresh[0]) & (scaled_sobel <= sx_thresh[1])] = 1
# convert to LUV color space
luv = cv2.cvtColor(img,cv2.COLOR_RGB2Luv)
luv_channel = luv[:,:,0]
u_channel = luv[:,:,1]
v_channel = luv[:,:,2]
luv_binary = np.zeros_like(luv_channel)
# luv threshold
l_th = (luv_channel >= l_thresh[0]) & (luv_channel <=l_thresh[1])
u_th = (u_channel >= u_threshold[0]) & (u_channel <=u_threshold[1])
v_th = (v_channel >= v_threshold[0]) & (v_channel <=v_threshold[1])
luv_binary[l_th & u_th & v_th] =1
# convert to LAB color space
lab = cv2.cvtColor(img,cv2.COLOR_RGB2Lab)
lab_channel = lab[:,:,0]
a_channel = lab[:,:,1]
b_channel = lab[:,:,2]
lab_binary = np.zeros_like(b_channel)
# LAB threshold
lab_th = (lab_channel >= lab_threshold[0]) & (lab_channel <=lab_threshold[1])
a_th = (a_channel >= a_threshold[0]) & (a_channel <=a_threshold[1])
b_th = (b_channel >= b_thresh[0]) & (b_channel <=b_thresh[1])
lab_binary[lab_th & a_th & b_th] = 1
# combined channel
combined = np.zeros_like(sxbinary)
combined[(sxbinary == 1) | (luv_binary == 1) | (lab_binary ==1)] = 1
return combined
Perspective transform¶
In [7]:
def perspective_transform(img):
# Grab the image shape
img_size = (img.shape[1], img.shape[0])
# Hardcode the source and destination points
src = np.float32(
[[(img_size[0]/2) - 55, img_size[1]/2 + 100],
[((img_size[0]/6)-10), img_size[1]],
[(img_size[0]*5/6)+60, img_size[1]],
[(img_size[0]/2+55), img_size[1]/2+100]])
dst = np.float32(
[[(img_size[0]/4), 0],
[(img_size[0]/4), img_size[1]],
[(img_size[0]*3/4), img_size[1]],
[(img_size[0]*3/4), 0]])
# Compute the perspective transform, M, given source and destination points:
M = cv2.getPerspectiveTransform(src, dst)
# Compute the inverse perspective transform:
Minv = cv2.getPerspectiveTransform(dst, src)
# Warp the image using OpenCV warpPerspective()
warped = cv2.warpPerspective(img, M, img_size, flags=cv2.INTER_LINEAR)
# Return the resulting image and matrix
return warped, M, Minv
Identify lane line: implement sliding windows and fit a polynomial¶
In [8]:
def find_lane_pixels(binary_warped):
# Take a histogram of the bottom half of the image
histogram = np.sum(binary_warped[binary_warped.shape[0]//2:,:], axis=0)
# Create an output image to draw on and visualize the result
out_img = np.dstack((binary_warped, binary_warped, binary_warped))
# Find the peak of the left and right halves of the histogram
# These will be the starting point for the left and right lines
midpoint = np.int(histogram.shape[0]//2)
leftx_base = np.argmax(histogram[:midpoint])
rightx_base = np.argmax(histogram[midpoint:]) + midpoint
# HYPERPARAMETERS
# Choose the number of sliding windows
nwindows = 9
# Set the width of the windows +/- margin
margin = 100
# Set minimum number of pixels found to recenter window
minpix = 50
# Set height of windows - based on nwindows above and image shape
window_height = np.int(binary_warped.shape[0]//nwindows)
# Identify the x and y positions of all nonzero pixels in the image
nonzero = binary_warped.nonzero()
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
# Current positions to be updated later for each window in nwindows
leftx_current = leftx_base
rightx_current = rightx_base
# Create empty lists to receive left and right lane pixel indices
left_lane_inds = []
right_lane_inds = []
# Step through the windows one by one
for window in range(nwindows):
# Identify window boundaries in x and y (and right and left)
win_y_low = binary_warped.shape[0] - (window+1)*window_height
win_y_high = binary_warped.shape[0] - window*window_height
### TO-DO: Find the four below boundaries of the window ###
win_xleft_low = leftx_current - margin
win_xleft_high = leftx_current + margin
win_xright_low = rightx_current - margin
win_xright_high = rightx_current + margin
# Draw the windows on the visualization image
cv2.rectangle(out_img,(win_xleft_low,win_y_low),
(win_xleft_high,win_y_high),(0,255,0), 2)
cv2.rectangle(out_img,(win_xright_low,win_y_low),
(win_xright_high,win_y_high),(0,255,0), 2)
### TO-DO: Identify the nonzero pixels in x and y within the window ###
good_left_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) &
(nonzerox >= win_xleft_low) & (nonzerox < win_xleft_high)).nonzero()[0]
good_right_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) &
(nonzerox >= win_xright_low) & (nonzerox < win_xright_high)).nonzero()[0]
# Append these indices to the lists
left_lane_inds.append(good_left_inds)
right_lane_inds.append(good_right_inds)
### TO-DO: If you found > minpix pixels, recenter next window ###
### (`right` or `leftx_current`) on their mean position ###
pass # Remove this when you add your function
# Concatenate the arrays of indices (previously was a list of lists of pixels)
try:
left_lane_inds = np.concatenate(left_lane_inds)
right_lane_inds = np.concatenate(right_lane_inds)
except ValueError:
# Avoids an error if the above is not implemented fully
pass
# Extract left and right line pixel positions
leftx = nonzerox[left_lane_inds]
lefty = nonzeroy[left_lane_inds]
rightx = nonzerox[right_lane_inds]
righty = nonzeroy[right_lane_inds]
return leftx, lefty, rightx, righty, out_img
def fit_polynomial(binary_warped, show=0):
# Find our lane pixels first
leftx, lefty, rightx, righty, out_img = find_lane_pixels(binary_warped)
### TO-DO: Fit a second order polynomial to each using `np.polyfit` ###
if (len(leftx)>0) & (len(lefty)>0) & (len(rightx)>0) & (len(righty)>0):
left_fit = np.polyfit(lefty, leftx, 2)
right_fit = np.polyfit(righty, rightx, 2)
else:
left_fit = left_line.current_fit
right_fit = right_line.current_fit
# Generate x and y values for plotting
ploty = np.linspace(0, binary_warped.shape[0]-1, binary_warped.shape[0] )
try:
left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
except TypeError:
# Avoids an error if `left` and `right_fit` are still none or incorrect
print('The function failed to fit a line!')
left_fitx = 1*ploty**2 + 1*ploty
right_fitx = 1*ploty**2 + 1*ploty
if(show == 0):
## Visualization ##
# Colors in the left and right lane regions
out_img[lefty, leftx] = [255, 0, 0]
out_img[righty, rightx] = [0, 0, 255]
left_line.current_fit = left_fit
right_line.current_fit = right_fit
left_line.detected = True
right_line.detected = True
return out_img, ploty, left_fitx, right_fitx, left_fit, right_fit
Calculated lane curvature and vehicle position¶
In [9]:
# Define conversions in x and y from pixels space to meters
ym_per_pix = 30/720 # meters per pixel in y dimension
def cal_curvature(img, fit, xm_per_pix):
y = np.linspace(0, img.shape[0]-1, img.shape[0])
x = fit[0] * y**2 + fit[1] * y + fit[2]
y_eval = y[-1]
fit_cr = np.polyfit(y*ym_per_pix, x*xm_per_pix, 2)
# Calculate the new radii of curvature
curverad = ((1 + (2*fit_cr[0]*y_eval*ym_per_pix + fit_cr[1])**2)**1.5) / np.absolute(2*fit_cr[0])
curverad = np.round(curverad, 2)
return curverad
def cal_lane_curv(img, left_fit, right_fit):
left_c = left_fit[0] * img.shape[0] ** 2 + left_fit[1] * img.shape[0] + left_fit[2]
right_c = right_fit[0] * img.shape[0] ** 2 + right_fit[1] * img.shape[0] + right_fit[2]
width = right_c - left_c
xm_per_pix = 3.7 / width
left_curvature = cal_curvature(img, left_fit, xm_per_pix)
right_curvature = cal_curvature(img, right_fit, xm_per_pix)
avg_fit = np.mean([left_fit, right_fit], axis = 0)
radius_of_curvature = cal_curvature(img, avg_fit, xm_per_pix)
return radius_of_curvature, xm_per_pix
def cal_offset(img, left_fit, right_fit, xm_per_pix):
y_eval = img.shape[0] - 1;
left_x = left_fit[0] * y_eval**2 + left_fit[1] * y_eval + left_fit[2]
right_x = right_fit[0] * y_eval**2 + right_fit[1] * y_eval + right_fit[2]
car_center = (left_x + right_x)/2
lane_center = img.shape[1] / 2
offset = (car_center - lane_center) * xm_per_pix
return offset
Drawing¶
In [10]:
def draw_figure(undistorted, warped, left_fit, right_fit, Minv, radius_of_curvature, offset):
# Create an image to draw the lines on
warp_zero = np.zeros_like(warped).astype(np.uint8)
color_warp = np.dstack((warp_zero, warp_zero, warp_zero))
ploty = np.linspace(0, warped.shape[0]-1, warped.shape[0])
try:
left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
except TypeError:
print('The function failed to fit a line!')
left_fitx = 1*ploty**2 + 1*ploty
right_fitx = 1*ploty**2 + 1*ploty
# Recast the x and y points into usable format for cv2.fillPoly()
pts_left = np.array([np.transpose(np.vstack([left_fitx, ploty]))])
pts_right = np.array([np.flipud(np.transpose(np.vstack([right_fitx, ploty])))])
pts = np.hstack((pts_left, pts_right))
# Draw the lane onto the warped blank image
cv2.fillPoly(color_warp, np.int_([pts]), (0,255, 0))
# Warp the blank back to original image space using inverse perspective matrix (Minv)
newwarp = cv2.warpPerspective(color_warp, Minv, (img.shape[1], img.shape[0]))
# Combine the result with the original image
result = cv2.addWeighted(undistorted, 1, newwarp, 0.3, 0)
cv2.putText(result,'Radius of Curvature: %.2fm' % radius_of_curvature, (20,40), cv2.FONT_HERSHEY_SIMPLEX, 1, (255,255,255) , 2)
if offset < 0:
text = 'left'
else:
text = 'right'
cv2.putText(result,'Distance From Center: %.2fm %s' % (np.absolute(offset), text), (20,80), cv2.FONT_HERSHEY_SIMPLEX, 1, (255,255,255), 2)
return result
Example¶
In [11]:
img = cv2.imread("test_images/straight_lines1.jpg")
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
plt.figure()
plt.title("Original image:")
plt.imshow(img)
plt.savefig("output_images/original.jpg")
undistorted = cal_undistort(img, objpoints, imgpoints)
plt.figure()
plt.title("Undistorted image:")
plt.imshow(undistorted)
plt.savefig("output_images/undistorted.jpg")
binary = binary_image(undistorted)
plt.figure()
plt.title("Binary image:")
plt.imshow(binary, cmap='gray')
plt.savefig("output_images/binary.jpg")
warped, M, Minv = perspective_transform(binary)
plt.figure()
plt.title("Perspective transform:")
plt.imshow(warped, cmap='gray')
plt.savefig("output_images/transform.jpg")
out_img, ploty, left_fitx, right_fitx, left_fit, right_fit = fit_polynomial(warped)
plt.figure()
plt.title("Sliding Windows and Fit a Polynomial:")
plt.plot(left_fitx, ploty, color='yellow')
plt.plot(right_fitx, ploty, color='yellow')
plt.imshow(out_img)
plt.savefig("output_images/fit.jpg")
radius_of_curvature, xm_per_pix = cal_lane_curv(warped, left_fit, right_fit)
offset = cal_offset(warped, left_fit, right_fit, xm_per_pix)
result = draw_figure(undistorted, warped, left_fit, right_fit, Minv, radius_of_curvature, offset)
plt.figure()
plt.title("Result:")
plt.imshow(result)
Out[11]:
Image pipeline¶
In [12]:
def process_image(image):
undistorted = cal_undistort(image, objpoints, imgpoints)
binary = binary_image(undistorted)
warped, M, Minv = perspective_transform(binary)
out_img, ploty, left_fitx, right_fitx, left_fit, right_fit = fit_polynomial(warped, 1)
radius_of_curvature, xm_per_pix = cal_lane_curv(warped, left_fit, right_fit)
offset = cal_offset(warped, left_fit, right_fit, xm_per_pix)
result = draw_figure(undistorted, warped, left_fit, right_fit, Minv, radius_of_curvature, offset)
return result
Result¶
In [13]:
files = os.listdir("test_images/")
files.sort()
def show(path, filename):
image = cv2.imread(path + filename)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
plt.figure()
plt.title(filename)
plt.imshow(image)
plt.savefig("output_images/original_" + filename)
def process(input_path, output_path, filename):
image = cv2.imread(input_path + filename)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
result = process_image(image)
plt.figure()
plt.imshow(result)
plt.savefig(output_path + filename)
for file in files:
show("test_images/", file)
process("test_images/", "output_images/", file)
Pipeline (video)¶
Look-Ahead Filter¶
In [14]:
def fit_poly(img_shape, leftx, lefty, rightx, righty):
### TO-DO: Fit a second order polynomial to each with np.polyfit() ###
if (len(leftx)>0) & (len(lefty)>0) & (len(rightx)>0) & (len(righty)>0):
left_fit = np.polyfit(lefty,leftx, 2)
right_fit = np.polyfit(righty,rightx, 2)
left_line.current_fit = left_fit
right_line.current_fit = right_fit
else:
left_fit = left_line.current_fit
right_fit = right_line.current_fit
# Generate x and y values for plotting
ploty = np.linspace(0, img_shape[0]-1, img_shape[0])
### TO-DO: Calc both polynomials using ploty, left_fit and right_fit ###
left_fitx = left_fit[0]*(ploty**2) + left_fit[1]*ploty + left_fit[2]
right_fitx = right_fit[0]*(ploty**2) + right_fit[1]*ploty + right_fit[2]
return left_fitx, right_fitx, ploty
def search_around_poly(binary_warped, show=0):
# HYPERPARAMETER
# Choose the width of the margin around the previous polynomial to search
# The quiz grader expects 100 here, but feel free to tune on your own!
margin = 100
# Grab activated pixels
nonzero = binary_warped.nonzero()
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
left_fit = left_line.current_fit
right_fit = right_line.current_fit
### TO-DO: Set the area of search based on activated x-values ###
### within the +/- margin of our polynomial function ###
### Hint: consider the window areas for the similarly named variables ###
### in the previous quiz, but change the windows to our new search area ###
left_lane_inds = ((nonzerox > (left_fit[0]*(nonzeroy**2) + left_fit[1]*nonzeroy +
left_fit[2] - margin)) & (nonzerox < (left_fit[0]*(nonzeroy**2) +
left_fit[1]*nonzeroy + left_fit[2] + margin)))
right_lane_inds = ((nonzerox > (right_fit[0]*(nonzeroy**2) + right_fit[1]*nonzeroy +
right_fit[2] - margin)) & (nonzerox < (right_fit[0]*(nonzeroy**2) +
right_fit[1]*nonzeroy + right_fit[2] + margin)))
# Again, extract left and right line pixel positions
leftx = nonzerox[left_lane_inds]
lefty = nonzeroy[left_lane_inds]
rightx = nonzerox[right_lane_inds]
righty = nonzeroy[right_lane_inds]
# Fit new polynomials
left_fitx, right_fitx, ploty = fit_poly(binary_warped.shape, leftx, lefty, rightx, righty)
## Visualization ##
# Create an image to draw on and an image to show the selection window
out_img = np.dstack((binary_warped, binary_warped, binary_warped))*255
window_img = np.zeros_like(out_img)
# Color in left and right line pixels
out_img[nonzeroy[left_lane_inds], nonzerox[left_lane_inds]] = [255, 0, 0]
out_img[nonzeroy[right_lane_inds], nonzerox[right_lane_inds]] = [0, 0, 255]
# Generate a polygon to illustrate the search window area
# And recast the x and y points into usable format for cv2.fillPoly()
left_line_window1 = np.array([np.transpose(np.vstack([left_fitx-margin, ploty]))])
left_line_window2 = np.array([np.flipud(np.transpose(np.vstack([left_fitx+margin, ploty])))])
left_line_pts = np.hstack((left_line_window1, left_line_window2))
right_line_window1 = np.array([np.transpose(np.vstack([right_fitx-margin, ploty]))])
right_line_window2 = np.array([np.flipud(np.transpose(np.vstack([right_fitx+margin, ploty])))])
right_line_pts = np.hstack((right_line_window1, right_line_window2))
# Draw the lane onto the warped blank image
cv2.fillPoly(window_img, np.int_([left_line_pts]), (0,255, 0))
cv2.fillPoly(window_img, np.int_([right_line_pts]), (0,255, 0))
result = cv2.addWeighted(out_img, 1, window_img, 0.3, 0)
if(show == 0):
# Plot the polynomial lines onto the image
plt.plot(left_fitx, ploty, color='yellow')
plt.plot(right_fitx, ploty, color='yellow')
## End visualization steps ##
return result, ploty, left_fitx, right_fitx, left_fit, right_fit
Video pipeline¶
In [15]:
left_line.detected = False
right_line.detected = False
left_line.current_fit = [np.array([False])]
right_line.current_fit = [np.array([False])]
def check_lane(left_fit,right_fit, h, w, radius_of_curvature, offset):
# the slope difference between left_line slope and right_line
if np.abs((left_fit[0]*h+left_fit[1])-(right_fit[0]*h+right_fit[1])) > (w/h):
return False
# the distance between right_bottom and left_bottom
if (right_fit[0]*h**2+right_fit[1]*h+right_fit[2])-(left_fit[0]*h**2+left_fit[1]*h+left_fit[2]) < (w/2):
return False
return True
def process_frame(image):
undistorted = cal_undistort(image, objpoints, imgpoints)
binary = binary_image(undistorted)
warped, M, Minv = perspective_transform(binary)
if (left_line.detected == False) | (right_line.detected == False):
out_img, ploty, left_fitx, right_fitx, left_fit, right_fit = fit_polynomial(warped, 1)
else:
out_img, ploty, left_fitx, right_fitx, left_fit, right_fit = search_around_poly(warped, 1)
radius_of_curvature, xm_per_pix = cal_lane_curv(warped, left_fit, right_fit)
offset = cal_offset(warped, left_fit, right_fit, xm_per_pix)
if (check_lane(left_fit,right_fit, warped.shape[0], warped.shape[1], radius_of_curvature, offset) == False):
left_line.detected = False
right_line.detected = False
result = draw_figure(undistorted, warped, left_fit, right_fit, Minv, radius_of_curvature, offset)
return result
Video example¶
In [16]:
# Import everything needed to edit/save/watch video clips
from moviepy.editor import VideoFileClip
from IPython.display import HTML
video_output = 'project_video_output.mp4'
clip1 = VideoFileClip("project_video.mp4")
video_clip = clip1.fl_image(process_frame)
%time video_clip.write_videofile(video_output, audio=False)
In [17]:
HTML("""
<video width="960" height="540" controls>
<source src="{0}">
</video>
""".format(video_output))
Out[17]:
In [18]:
# Import everything needed to edit/save/watch video clips
from moviepy.editor import VideoFileClip
from IPython.display import HTML
video_output = 'challenge_video_output.mp4'
clip1 = VideoFileClip("challenge_video.mp4")
video_clip = clip1.fl_image(process_frame)
%time video_clip.write_videofile(video_output, audio=False)
In [19]:
HTML("""
<video width="960" height="540" controls>
<source src="{0}">
</video>
""".format(video_output))
Out[19]: